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Article Citation - WoS: 25Citation - Scopus: 28Analysis of Mirror Neuron System Activation During Action Observation Alone and Action Observation With Motor Imagery Tasks(Springer, 2018) Cengiz, Bulent; Vuralli, Doga; Zinnuroglu, Murat; Bayer, Gozde; Golmohammadzadeh, Hassan; Gunendi, Zafer; Arikan, Kutluk BilgeThis study aimed to explore the relationship between action observation (AO)-related corticomotor excitability changes and phases of observed action and to explore the effects of pure AO and concurrent AO and motor imagery (MI) state on corticomotor excitability using TMS. It was also investigated whether the mirror neuron system activity is muscle-specific. Fourteen healthy volunteers were enrolled in the study. EMG recordings were taken from the right first dorsal interosseous and the abductor digiti minimi muscles. There was a significant main effect of TMS timing (after the beginning of the movement, at the beginning of motor output state, and during black screen) on the mean motor evoked potential (MEP) amplitude. Mean MEP amplitudes for AO combined with MI were significantly higher than pure AO session. There was a significant interaction between session and TMS timing. There was no significant main effect of muscle on MEP amplitude. The results indicate that corticomotor excitability is modulated by different phases of the observed motor movement and this modulation is not muscle-specific. Simultaneous MI and AO enhance corticomotor excitability significantly compared to pure AO.Article Citation - WoS: 51Citation - Scopus: 64Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method(Sage Publications Ltd, 2014) Arvin, Farshad; Turgut, Ali Emre; Bazyari, Farhad; Arikan, Kutluk Bilge; Bellotto, Nicola; Yue, ShigangAggregation in swarm robotics is referred to as the gathering of spatially distributed robots into a single aggregate. Aggregation can be classified as cue-based or self-organized. In cue-based aggregation, there is a cue in the environment that points to the aggregation area, whereas in self-organized aggregation no cue is present. In this paper, we proposed a novel fuzzy-based method for cue-based aggregation based on the state-of-the-art BEECLUST algorithm. In particular, we proposed three different methods: naive, that uses a deterministic decision-making mechanism; vector-averaging, using a vectorial summation of all perceived inputs; and fuzzy, that uses a fuzzy logic controller. We used different experiment settings: one-source and two-source environments with static and dynamic conditions to compare all the methods. We observed that the fuzzy method outperformed all the other methods and it is the most robust method against noise.
